本质有缺陷，进化臻完美
Flawed in Nature, Perfect through Evolution
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中文总结 AI 辅助
该研究提出“本质有缺陷，进化臻完美”机制，通过突变模型系数使模型群对冲环境非平稳性，可减少遗憾，在合成任务中约80%环境变化下生成最优模型，为AI系统提供新设计原则。
中文摘要 AI 辅助
当人工智能（AI）和机器学习（ML）模型所训练的问题发生漂移时，其性能会下降，这是现实世界问题的近乎普遍特征，这类问题往往会不可预测地发生变化。生物进化通过对可遗传变异进行自然选择克服了这一障碍，从而实现了智能。AI/ML技术早已融入了自然选择的各种形式，但由于优化过程自然会驱动收敛，维持模型多样性一直颇具挑战。本文表明，一组AI/ML模型在其模型系数偏离最优值时受到刻意突变的影响，能够通过作为非平稳性的统计对冲，在变化的环境中可靠且持续地提升性能。我们将这一机制称为“本质有缺陷，进化臻完美”，反映出集体性能的提升是以牺牲个体性能为代价的。我们通过四个定理证明，在一般条件下可保证遗憾减少，从而确立了“本质有缺陷”机制作为AI/ML系统可推广的设计原则。我们在合成线性回归任务上验证了这些结果，表明突变模型群在约80%的环境变化中能产生最优模型，且推理合成成功将这一个体优势转化为集体优势。当突变漂移率与环境漂移率匹配时，该机制被证明最为有效。我们概述了一种简单的自适应控制器，通过调整突变漂移率以匹配环境的未知漂移率，从而实现实际应用。“本质有缺陷”机制与生物进化的密切类比表明，它可能是有机发现更接近生物智能的AI形式的关键缺失要素。
英文摘要
The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem they were trained on drifts. This is a near-universal feature of real-world problems, which often change unpredictably. Biological evolution has achieved intelligence by overcoming this obstacle through natural selection acting on heritable variation. AI/ML techniques have long incorporated forms of natural selection, but it has been challenging to maintain model diversity as optimization naturally drives convergence. Here we show that a swarm of AI/ML models subjected to deliberate mutations of their model coefficients away from optimality can reliably and sustainably improve performance in changing environments by acting as a statistical hedge against non-stationarity. We call this mechanism 'Flawed in Nature, Perfect through Evolution', reflecting that the collective performance gain goes at the expense of individual performance. We prove via four theorems that the resulting regret reduction is guaranteed under general conditions, establishing the Flawed-in-Nature mechanism as a generalizable design principle for AI/ML systems. We validate these results on synthetic linear regression tasks, demonstrating that the mutated swarm delivers the best model in $\sim80\%$ of environment changes and that inference synthesis successfully translates this individual advantage into a collective one. The mechanism proves to be most effective when the mutation drift rate matches the drift rate of the environment. We outline a simple, adaptive controller that enables practical applications by tuning the mutation drift rate to match the unknown drift rate of the environment. The close analogy of the Flawed-in-Nature mechanism to biological evolution suggests it may have been a critical missing ingredient for the organic discovery of AI forms that more closely mimic biological intelligence.
发表机构
- Allora Foundation（阿罗拉基金会）
机构由 AI 辅助整理，请以论文原文为准。
